design wavelet matlab filter models Search Results


filter design and analysis tool (fdatool)  (MathWorks Inc)
90
MathWorks Inc filter design and analysis tool (fdatool)
Filter Design And Analysis Tool (Fdatool), supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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fda tool  (MathWorks Inc)
90
MathWorks Inc fda tool
Fda Tool, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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fda tool - by Bioz Stars, 2026-05
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6-order butterworth bandpass filters  (MathWorks Inc)
90
MathWorks Inc 6-order butterworth bandpass filters
Flowchart of EEG signal processing. First, we wanted to observe the differences between the brainwaves of normal, moderately, and markedly schizophrenic patients. Therefore, we put all signals into a matrix. The PCA method is used to decompose the matrix. Then, the signal is separated into five frequency bands using <t>IIR</t> filters <t>(6-order</t> <t>Butterworth</t> bandpass filters designed with the Matlab <t>R2015b</t> “Signal Processing Toolbox”): θ (4–8 Hz), α (8–12 Hz), β 1 (12–15 Hz), β 2 (15–18 Hz), and β 3 (18–30 Hz). We separate all different frequency bands of the signals into nine signal fragments according to the timeline that evoked stimuli. Then, we calculate the signals using the ApEn entropy methods. Finally, the obtained features are placed into the SVM (PCA on 95%) for classification. The predictive accuracy was evaluated using the 27-fold cross validation method with a quadratic kernel, in Part A. After that, we classify the features from three points of brainwaves, three types of visual stimuli (HVLA, LVLA, and LVHA), and three methods of entropy (ApEn, PE, and AAPE) in schizophrenic patients. Finally, using linear simple regression and independent-samples t test statistical analysis, we analyze the features of the highest identification degree in the classification results and the total scores of PANSS, in Part B
6 Order Butterworth Bandpass Filters, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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wavelet-based filter biorthogonal 3.3  (MathWorks Inc)
90
MathWorks Inc wavelet-based filter biorthogonal 3.3
Flowchart of EEG signal processing. First, we wanted to observe the differences between the brainwaves of normal, moderately, and markedly schizophrenic patients. Therefore, we put all signals into a matrix. The PCA method is used to decompose the matrix. Then, the signal is separated into five frequency bands using <t>IIR</t> filters <t>(6-order</t> <t>Butterworth</t> bandpass filters designed with the Matlab <t>R2015b</t> “Signal Processing Toolbox”): θ (4–8 Hz), α (8–12 Hz), β 1 (12–15 Hz), β 2 (15–18 Hz), and β 3 (18–30 Hz). We separate all different frequency bands of the signals into nine signal fragments according to the timeline that evoked stimuli. Then, we calculate the signals using the ApEn entropy methods. Finally, the obtained features are placed into the SVM (PCA on 95%) for classification. The predictive accuracy was evaluated using the 27-fold cross validation method with a quadratic kernel, in Part A. After that, we classify the features from three points of brainwaves, three types of visual stimuli (HVLA, LVLA, and LVHA), and three methods of entropy (ApEn, PE, and AAPE) in schizophrenic patients. Finally, using linear simple regression and independent-samples t test statistical analysis, we analyze the features of the highest identification degree in the classification results and the total scores of PANSS, in Part B
Wavelet Based Filter Biorthogonal 3.3, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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wavelet-based filter biorthogonal 3.3 - by Bioz Stars, 2026-05
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butterworth filter design-matlab butter  (MathWorks Inc)
90
MathWorks Inc butterworth filter design-matlab butter
Flowchart of EEG signal processing. First, we wanted to observe the differences between the brainwaves of normal, moderately, and markedly schizophrenic patients. Therefore, we put all signals into a matrix. The PCA method is used to decompose the matrix. Then, the signal is separated into five frequency bands using <t>IIR</t> filters <t>(6-order</t> <t>Butterworth</t> bandpass filters designed with the Matlab <t>R2015b</t> “Signal Processing Toolbox”): θ (4–8 Hz), α (8–12 Hz), β 1 (12–15 Hz), β 2 (15–18 Hz), and β 3 (18–30 Hz). We separate all different frequency bands of the signals into nine signal fragments according to the timeline that evoked stimuli. Then, we calculate the signals using the ApEn entropy methods. Finally, the obtained features are placed into the SVM (PCA on 95%) for classification. The predictive accuracy was evaluated using the 27-fold cross validation method with a quadratic kernel, in Part A. After that, we classify the features from three points of brainwaves, three types of visual stimuli (HVLA, LVLA, and LVHA), and three methods of entropy (ApEn, PE, and AAPE) in schizophrenic patients. Finally, using linear simple regression and independent-samples t test statistical analysis, we analyze the features of the highest identification degree in the classification results and the total scores of PANSS, in Part B
Butterworth Filter Design Matlab Butter, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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filter designer tool  (MathWorks Inc)
93
MathWorks Inc filter designer tool
Flowchart of EEG signal processing. First, we wanted to observe the differences between the brainwaves of normal, moderately, and markedly schizophrenic patients. Therefore, we put all signals into a matrix. The PCA method is used to decompose the matrix. Then, the signal is separated into five frequency bands using <t>IIR</t> filters <t>(6-order</t> <t>Butterworth</t> bandpass filters designed with the Matlab <t>R2015b</t> “Signal Processing Toolbox”): θ (4–8 Hz), α (8–12 Hz), β 1 (12–15 Hz), β 2 (15–18 Hz), and β 3 (18–30 Hz). We separate all different frequency bands of the signals into nine signal fragments according to the timeline that evoked stimuli. Then, we calculate the signals using the ApEn entropy methods. Finally, the obtained features are placed into the SVM (PCA on 95%) for classification. The predictive accuracy was evaluated using the 27-fold cross validation method with a quadratic kernel, in Part A. After that, we classify the features from three points of brainwaves, three types of visual stimuli (HVLA, LVLA, and LVHA), and three methods of entropy (ApEn, PE, and AAPE) in schizophrenic patients. Finally, using linear simple regression and independent-samples t test statistical analysis, we analyze the features of the highest identification degree in the classification results and the total scores of PANSS, in Part B
Filter Designer Tool, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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custom designed filter in matlab's fdatool  (MathWorks Inc)
90
MathWorks Inc custom designed filter in matlab's fdatool
Flowchart of EEG signal processing. First, we wanted to observe the differences between the brainwaves of normal, moderately, and markedly schizophrenic patients. Therefore, we put all signals into a matrix. The PCA method is used to decompose the matrix. Then, the signal is separated into five frequency bands using <t>IIR</t> filters <t>(6-order</t> <t>Butterworth</t> bandpass filters designed with the Matlab <t>R2015b</t> “Signal Processing Toolbox”): θ (4–8 Hz), α (8–12 Hz), β 1 (12–15 Hz), β 2 (15–18 Hz), and β 3 (18–30 Hz). We separate all different frequency bands of the signals into nine signal fragments according to the timeline that evoked stimuli. Then, we calculate the signals using the ApEn entropy methods. Finally, the obtained features are placed into the SVM (PCA on 95%) for classification. The predictive accuracy was evaluated using the 27-fold cross validation method with a quadratic kernel, in Part A. After that, we classify the features from three points of brainwaves, three types of visual stimuli (HVLA, LVLA, and LVHA), and three methods of entropy (ApEn, PE, and AAPE) in schizophrenic patients. Finally, using linear simple regression and independent-samples t test statistical analysis, we analyze the features of the highest identification degree in the classification results and the total scores of PANSS, in Part B
Custom Designed Filter In Matlab's Fdatool, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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function fspecial  (MathWorks Inc)
96
MathWorks Inc function fspecial
Flowchart of EEG signal processing. First, we wanted to observe the differences between the brainwaves of normal, moderately, and markedly schizophrenic patients. Therefore, we put all signals into a matrix. The PCA method is used to decompose the matrix. Then, the signal is separated into five frequency bands using <t>IIR</t> filters <t>(6-order</t> <t>Butterworth</t> bandpass filters designed with the Matlab <t>R2015b</t> “Signal Processing Toolbox”): θ (4–8 Hz), α (8–12 Hz), β 1 (12–15 Hz), β 2 (15–18 Hz), and β 3 (18–30 Hz). We separate all different frequency bands of the signals into nine signal fragments according to the timeline that evoked stimuli. Then, we calculate the signals using the ApEn entropy methods. Finally, the obtained features are placed into the SVM (PCA on 95%) for classification. The predictive accuracy was evaluated using the 27-fold cross validation method with a quadratic kernel, in Part A. After that, we classify the features from three points of brainwaves, three types of visual stimuli (HVLA, LVLA, and LVHA), and three methods of entropy (ApEn, PE, and AAPE) in schizophrenic patients. Finally, using linear simple regression and independent-samples t test statistical analysis, we analyze the features of the highest identification degree in the classification results and the total scores of PANSS, in Part B
Function Fspecial, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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2nd order low-pass digital butterworth filter  (MathWorks Inc)
90
MathWorks Inc 2nd order low-pass digital butterworth filter
Flowchart of EEG signal processing. First, we wanted to observe the differences between the brainwaves of normal, moderately, and markedly schizophrenic patients. Therefore, we put all signals into a matrix. The PCA method is used to decompose the matrix. Then, the signal is separated into five frequency bands using <t>IIR</t> filters <t>(6-order</t> <t>Butterworth</t> bandpass filters designed with the Matlab <t>R2015b</t> “Signal Processing Toolbox”): θ (4–8 Hz), α (8–12 Hz), β 1 (12–15 Hz), β 2 (15–18 Hz), and β 3 (18–30 Hz). We separate all different frequency bands of the signals into nine signal fragments according to the timeline that evoked stimuli. Then, we calculate the signals using the ApEn entropy methods. Finally, the obtained features are placed into the SVM (PCA on 95%) for classification. The predictive accuracy was evaluated using the 27-fold cross validation method with a quadratic kernel, in Part A. After that, we classify the features from three points of brainwaves, three types of visual stimuli (HVLA, LVLA, and LVHA), and three methods of entropy (ApEn, PE, and AAPE) in schizophrenic patients. Finally, using linear simple regression and independent-samples t test statistical analysis, we analyze the features of the highest identification degree in the classification results and the total scores of PANSS, in Part B
2nd Order Low Pass Digital Butterworth Filter, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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filter design function  (MathWorks Inc)
90
MathWorks Inc filter design function
Flowchart of EEG signal processing. First, we wanted to observe the differences between the brainwaves of normal, moderately, and markedly schizophrenic patients. Therefore, we put all signals into a matrix. The PCA method is used to decompose the matrix. Then, the signal is separated into five frequency bands using <t>IIR</t> filters <t>(6-order</t> <t>Butterworth</t> bandpass filters designed with the Matlab <t>R2015b</t> “Signal Processing Toolbox”): θ (4–8 Hz), α (8–12 Hz), β 1 (12–15 Hz), β 2 (15–18 Hz), and β 3 (18–30 Hz). We separate all different frequency bands of the signals into nine signal fragments according to the timeline that evoked stimuli. Then, we calculate the signals using the ApEn entropy methods. Finally, the obtained features are placed into the SVM (PCA on 95%) for classification. The predictive accuracy was evaluated using the 27-fold cross validation method with a quadratic kernel, in Part A. After that, we classify the features from three points of brainwaves, three types of visual stimuli (HVLA, LVLA, and LVHA), and three methods of entropy (ApEn, PE, and AAPE) in schizophrenic patients. Finally, using linear simple regression and independent-samples t test statistical analysis, we analyze the features of the highest identification degree in the classification results and the total scores of PANSS, in Part B
Filter Design Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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filter designer  (MathWorks Inc)
90
MathWorks Inc filter designer
Flowchart of EEG signal processing. First, we wanted to observe the differences between the brainwaves of normal, moderately, and markedly schizophrenic patients. Therefore, we put all signals into a matrix. The PCA method is used to decompose the matrix. Then, the signal is separated into five frequency bands using <t>IIR</t> filters <t>(6-order</t> <t>Butterworth</t> bandpass filters designed with the Matlab <t>R2015b</t> “Signal Processing Toolbox”): θ (4–8 Hz), α (8–12 Hz), β 1 (12–15 Hz), β 2 (15–18 Hz), and β 3 (18–30 Hz). We separate all different frequency bands of the signals into nine signal fragments according to the timeline that evoked stimuli. Then, we calculate the signals using the ApEn entropy methods. Finally, the obtained features are placed into the SVM (PCA on 95%) for classification. The predictive accuracy was evaluated using the 27-fold cross validation method with a quadratic kernel, in Part A. After that, we classify the features from three points of brainwaves, three types of visual stimuli (HVLA, LVLA, and LVHA), and three methods of entropy (ApEn, PE, and AAPE) in schizophrenic patients. Finally, using linear simple regression and independent-samples t test statistical analysis, we analyze the features of the highest identification degree in the classification results and the total scores of PANSS, in Part B
Filter Designer, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/filter designer/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
filter designer - by Bioz Stars, 2026-05
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matlab v6.12  (MathWorks Inc)
90
MathWorks Inc matlab v6.12
Flowchart of EEG signal processing. First, we wanted to observe the differences between the brainwaves of normal, moderately, and markedly schizophrenic patients. Therefore, we put all signals into a matrix. The PCA method is used to decompose the matrix. Then, the signal is separated into five frequency bands using <t>IIR</t> filters <t>(6-order</t> <t>Butterworth</t> bandpass filters designed with the Matlab <t>R2015b</t> “Signal Processing Toolbox”): θ (4–8 Hz), α (8–12 Hz), β 1 (12–15 Hz), β 2 (15–18 Hz), and β 3 (18–30 Hz). We separate all different frequency bands of the signals into nine signal fragments according to the timeline that evoked stimuli. Then, we calculate the signals using the ApEn entropy methods. Finally, the obtained features are placed into the SVM (PCA on 95%) for classification. The predictive accuracy was evaluated using the 27-fold cross validation method with a quadratic kernel, in Part A. After that, we classify the features from three points of brainwaves, three types of visual stimuli (HVLA, LVLA, and LVHA), and three methods of entropy (ApEn, PE, and AAPE) in schizophrenic patients. Finally, using linear simple regression and independent-samples t test statistical analysis, we analyze the features of the highest identification degree in the classification results and the total scores of PANSS, in Part B
Matlab V6.12, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Flowchart of EEG signal processing. First, we wanted to observe the differences between the brainwaves of normal, moderately, and markedly schizophrenic patients. Therefore, we put all signals into a matrix. The PCA method is used to decompose the matrix. Then, the signal is separated into five frequency bands using IIR filters (6-order Butterworth bandpass filters designed with the Matlab R2015b “Signal Processing Toolbox”): θ (4–8 Hz), α (8–12 Hz), β 1 (12–15 Hz), β 2 (15–18 Hz), and β 3 (18–30 Hz). We separate all different frequency bands of the signals into nine signal fragments according to the timeline that evoked stimuli. Then, we calculate the signals using the ApEn entropy methods. Finally, the obtained features are placed into the SVM (PCA on 95%) for classification. The predictive accuracy was evaluated using the 27-fold cross validation method with a quadratic kernel, in Part A. After that, we classify the features from three points of brainwaves, three types of visual stimuli (HVLA, LVLA, and LVHA), and three methods of entropy (ApEn, PE, and AAPE) in schizophrenic patients. Finally, using linear simple regression and independent-samples t test statistical analysis, we analyze the features of the highest identification degree in the classification results and the total scores of PANSS, in Part B

Journal: Annals of General Psychiatry

Article Title: Analysis of EEG entropy during visual evocation of emotion in schizophrenia

doi: 10.1186/s12991-017-0157-z

Figure Lengend Snippet: Flowchart of EEG signal processing. First, we wanted to observe the differences between the brainwaves of normal, moderately, and markedly schizophrenic patients. Therefore, we put all signals into a matrix. The PCA method is used to decompose the matrix. Then, the signal is separated into five frequency bands using IIR filters (6-order Butterworth bandpass filters designed with the Matlab R2015b “Signal Processing Toolbox”): θ (4–8 Hz), α (8–12 Hz), β 1 (12–15 Hz), β 2 (15–18 Hz), and β 3 (18–30 Hz). We separate all different frequency bands of the signals into nine signal fragments according to the timeline that evoked stimuli. Then, we calculate the signals using the ApEn entropy methods. Finally, the obtained features are placed into the SVM (PCA on 95%) for classification. The predictive accuracy was evaluated using the 27-fold cross validation method with a quadratic kernel, in Part A. After that, we classify the features from three points of brainwaves, three types of visual stimuli (HVLA, LVLA, and LVHA), and three methods of entropy (ApEn, PE, and AAPE) in schizophrenic patients. Finally, using linear simple regression and independent-samples t test statistical analysis, we analyze the features of the highest identification degree in the classification results and the total scores of PANSS, in Part B

Article Snippet: Then, the signal is separated into five frequency bands using IIR filters (6-order Butterworth bandpass filters designed with the Matlab R2015b “Signal Processing Toolbox”): θ (4–8 Hz), α (8–12 Hz), β 1 (12–15 Hz), β (15–18 Hz), and β 3 (18–30 Hz).

Techniques: Biomarker Discovery